探寻python多线程ctrl+c退出问题解决方案
场景:
经常会遇到下述问题:很多io busy的应用采取多线程的方式来解决,但这时候会发现python命令行不响应ctrl-c 了,而对应的java代码则没有问题:
代码如下:
public class Test {
public static void main(String[] args) throws Exception {
new Thread(new Runnable() {
public void run() {
long start = System.currentTimeMillis();
while (true) {
try {
Thread.sleep(1000);
} catch (Exception e) {
}
System.out.println(System.currentTimeMillis());
if (System.currentTimeMillis() - start > 1000 * 100) break;
}
}
}).start();
}
}
java Test
ctrl-c则会结束程序
而对应的python代码:
代码如下:
# -*- coding: utf-8 -*-
import time
import threading
start=time.time()
def foreverLoop():
start=time.time()
while 1:
time.sleep(1)
print time.time()
if time.time()-start>100:
break
thread_=threading.Thread(target=foreverLoop)
#thread_.setDaemon(True)
thread_.start()
python p.py
后ctrl-c则完全不起作用了。
不成熟的分析:
首先单单设置 daemon 为 true 肯定不行,就不解释了。当daemon为 false 时,导入python线程库后实际上,threading会在主线程执行完毕后,检查是否有不是 daemon 的线程,有的化就wait,等待线程结束了,在主线程等待期间,所有发送到主线程的信号也会被阻测,可以在上述代码加入signal模块验证一下:
代码如下:
def sigint_handler(signum,frame):
print "main-thread exit"
sys.exit()
signal.signal(signal.SIGINT,sigint_handler)
在100秒内按下ctrl-c没有反应,只有当子线程结束后才会出现打印 "main-thread exit",可见 ctrl-c被阻测了
threading 中在主线程结束时进行的操作:
代码如下:
_shutdown = _MainThread()._exitfunc
def _exitfunc(self):
self._Thread__stop()
t = _pickSomeNonDaemonThread()
if t:
if __debug__:
self._note("%s: waiting for other threads", self)
while t:
t.join()
t = _pickSomeNonDaemonThread()
if __debug__:
self._note("%s: exiting", self)
self._Thread__delete()
对所有的非daemon线程进行join等待,其中join中可自行察看源码,又调用了wait,同上文分析 ,主线程等待到了一把锁上。
不成熟的解决:
只能把线程设成daemon才能让主线程不等待,能够接受ctrl-c信号,但是又不能让子线程立即结束,那么只能采用传统的轮询方法了,采用sleep间歇省点cpu吧:
代码如下:
# -*- coding: utf-8 -*-
import time,signal,traceback
import sys
import threading
start=time.time()
def foreverLoop():
start=time.time()
while 1:
time.sleep(1)
print time.time()
if time.time()-start>5:
break
thread_=threading.Thread(target=foreverLoop)
thread_.setDaemon(True)
thread_.start()
#主线程wait住了,不能接受信号了
#thread_.join()
def _exitCheckfunc():
print "ok"
try:
while 1:
alive=False
if thread_.isAlive():
alive=True
if not alive:
break
time.sleep(1)
#为了使得统计时间能够运行,要捕捉 KeyboardInterrupt :ctrl-c
except KeyboardInterrupt, e:
traceback.print_exc()
print "consume time :",time.time()-start
threading._shutdown=_exitCheckfunc
缺点:轮询总会浪费点cpu资源,以及battery.
有更好的解决方案敬请提出。
ps1: 进程监控解决方案 :
用另外一个进程来接受信号后杀掉执行任务进程,牛
代码如下:
# -*- coding: utf-8 -*-
import time,signal,traceback,os
import sys
import threading
start=time.time()
def foreverLoop():
start=time.time()
while 1:
time.sleep(1)
print time.time()
if time.time()-start>5:
break
class Watcher:
"""this class solves two problems with multithreaded
programs in Python, (1) a signal might be delivered
to any thread (which is just a malfeature) and (2) if
the thread that gets the signal is waiting, the signal
is ignored (which is a bug).
The watcher is a concurrent process (not thread) that
waits for a signal and the process that contains the
threads. See Appendix A of The Little Book of Semaphores.
http://greenteapress.com/semaphores/
I have only tested this on Linux. I would expect it to
work on the Macintosh and not work on Windows.
"""
def __init__(self):
""" Creates a child thread, which returns. The parent
thread waits for a KeyboardInterrupt and then kills
the child thread.
"""
self.child = os.fork()
if self.child == 0:
return
else:
self.watch()
def watch(self):
try:
os.wait()
except KeyboardInterrupt:
# I put the capital B in KeyBoardInterrupt so I can
# tell when the Watcher gets the SIGINT
print 'KeyBoardInterrupt'
self.kill()
sys.exit()
def kill(self):
try:
os.kill(self.child, signal.SIGKILL)
except OSError: pass
Watcher()
thread_=threading.Thread(target=foreverLoop)
thread_.start()
注意 watch()一定要放在线程创建前,原因未知。。。。,否则立刻就结束

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

The speed of mobile XML to PDF depends on the following factors: the complexity of XML structure. Mobile hardware configuration conversion method (library, algorithm) code quality optimization methods (select efficient libraries, optimize algorithms, cache data, and utilize multi-threading). Overall, there is no absolute answer and it needs to be optimized according to the specific situation.

An application that converts XML directly to PDF cannot be found because they are two fundamentally different formats. XML is used to store data, while PDF is used to display documents. To complete the transformation, you can use programming languages and libraries such as Python and ReportLab to parse XML data and generate PDF documents.

To generate images through XML, you need to use graph libraries (such as Pillow and JFreeChart) as bridges to generate images based on metadata (size, color) in XML. The key to controlling the size of the image is to adjust the values of the <width> and <height> tags in XML. However, in practical applications, the complexity of XML structure, the fineness of graph drawing, the speed of image generation and memory consumption, and the selection of image formats all have an impact on the generated image size. Therefore, it is necessary to have a deep understanding of XML structure, proficient in the graphics library, and consider factors such as optimization algorithms and image format selection.

It is impossible to complete XML to PDF conversion directly on your phone with a single application. It is necessary to use cloud services, which can be achieved through two steps: 1. Convert XML to PDF in the cloud, 2. Access or download the converted PDF file on the mobile phone.

There is no built-in sum function in C language, so it needs to be written by yourself. Sum can be achieved by traversing the array and accumulating elements: Loop version: Sum is calculated using for loop and array length. Pointer version: Use pointers to point to array elements, and efficient summing is achieved through self-increment pointers. Dynamically allocate array version: Dynamically allocate arrays and manage memory yourself, ensuring that allocated memory is freed to prevent memory leaks.

Use most text editors to open XML files; if you need a more intuitive tree display, you can use an XML editor, such as Oxygen XML Editor or XMLSpy; if you process XML data in a program, you need to use a programming language (such as Python) and XML libraries (such as xml.etree.ElementTree) to parse.

To convert XML images, you need to determine the XML data structure first, then select a suitable graphical library (such as Python's matplotlib) and method, select a visualization strategy based on the data structure, consider the data volume and image format, perform batch processing or use efficient libraries, and finally save it as PNG, JPEG, or SVG according to the needs.

XML formatting tools can type code according to rules to improve readability and understanding. When selecting a tool, pay attention to customization capabilities, handling of special circumstances, performance and ease of use. Commonly used tool types include online tools, IDE plug-ins, and command-line tools.
